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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 2,901 Documents
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Enabling SECS/GEM in legacy equipment: a proof of concept Syahir Kamal Fitri, Muhammad; Manickam, Selvakumar; Ul Arfeen Laghari, Shams; Kok Chia, Siang; Khairi Ishak, Mohamad; Karuppayah, Shankar
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9516

Abstract

The rapid adoption of Industry 4.0 (I4.0) has driven the need for automated machine-to-machine (M2M) communication in manufacturing. However, legacy equipment remains a challenge due to its incompatibility with modern protocols like semiconductor equipment and materials international (SEMI) equipment communication standard/generic equipment model (SECS/GEM). Replacing these machines is costly, making retrofitting a more viable solution. This paper proposes a modular automation software framework that enables SECS/GEM integration for legacy machines without extensive hardware modifications. The system is implemented using Raspberry Pi and Arduino, acting as an intermediary between legacy equipment and modern factory networks. The framework facilitates real-time data exchange, remote monitoring, and enhanced automation while ensuring scalability and cost-effectiveness. Experimental evaluation demonstrates improved interoperability and reduced manual intervention. This solution provides a practical and adaptable approach to integrating legacy systems into (I4.0) environments.
MAS-TENER: a modified attention score transformer encoder for Indonesian skill entity recognition Nonsi Tentua, Meilany; Suprapto, Suprapto; Afiahayati, Afiahayati
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9731

Abstract

Skill entity recognition is a crucial task for aligning educational curricula with the evolving needs of the industry, particularly in multilingual job markets. This study introduces modified attention score transformer encoder (MAS-TENER), a novel transformer-based model designed to enhance the recognition of skill entities from Indonesian job descriptions. The proposed model modifies the attention mechanism by integrating relative positional embeddings and removing the scaling factor in self-attention. These improvements enhance the context of tokens, allowing for the accurate establishment of hard skills, soft skills, and technology skills. The MAS-TENER model was pre-trained and fine-tuned using a combinF.ation of job description datasets and additional corpora, achieving an F1-score of 90.46% at the entity level. The experimental results demonstrate the model's ability to handle unstructured, mixed-language job descriptions, with significant potential for curriculum reform and the development of new workforce capabilities. The study demonstrates the efficacy of the MAS-TENER model as an effective response for any natural language processing (NLP) task in low-resource languages. Moreover, the scope of long-term job market analytics in action research has been a key skill set in the education policy arena, demonstrating collaborative workforce capabilities.
Explainable artificial intelligence for multiclass prediction model of suicide attempt Nordin, Noratikah; Noor, Mohd Halim Mohd; Zainol, Zurinahni; Fong, Chan Lai; Buji, Ryna Imma
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9837

Abstract

Suicide attempt prediction is a challenging classification problem that involves a variety of risk factors in individuals with various medical conditions. Accurate risk stratification prediction is hampered by the absence of reasons for those who have attempted suicide and developing prediction model is challenged to be explained. Therefore, this work aimed to develop a multiclass prediction model for suicide attempts and to use Shapley additive explanations (SHAP), an explainable artificial intelligence (XAI) method to analyze the prediction model for suicide attempts in explaining the decision of the model. The prediction model is trained using machine learning approaches, random forest (RF) and gradient boosting (GB), on a clinical dataset of patients with chronic diseases. GB demonstrated higher accuracy with 0.81 than RF with 0.78 for multiclass classification results (no risk, low risk, moderate risk, and high risk). By analyzing the SHAP explanations, clinicians can gain valuable insights into the factors contributing to suicide attempt predictions in patients with chronic diseases. This enhanced understanding can facilitate more informed and appropriate treatment decisions, potentially leading to improved patient outcomes and targeted interventions.
Design and development of harmonic filters for harmonics reduction in polluted distribution network R. Chavan, Pranita; R. Patil, Babasaheb
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9504

Abstract

Recently due to development in the power electronics sector, there is a tremendous increase in nonlinear loads. These nonlinear loads cause distortion in the system current and result in degrading quality of power. The poor power quality causes technical and financial losses in the system which necessitates adoption of techniques to reduce the harmonic distortion to meet IEEE-standards and improve system efficiency. As per literature, passive, active and hybrid filter techniques are implemented to mitigate the harmonics. Each has merits and demerits. Constructive reduction in current harmonics improves the life and efficiency of equipment’s also assists to improve power quality and relieves penalties imposed by utilities. In this work, an attempt is made to give a detailed approach used in the designing of harmonic filters. This study will provide a broad outline to the engineer, researcher and consultant working in the field of power quality to design filters for the case under study. The steps to design the filters are well explained with mathematical equations and examples for greater insight. To validate the performance of the filter a MATLAB/Simulink platform is utilized. The outcome of the simulation result proved that current harmonics are minimized with a substantial amount.
The A3C-CCTSO-R2N2 algorithmic framework for precise edge-cloud parameter estimation Manonmani, Gangadharan; Ponmozhi, Krishnasamy; Balasubramanian, Krishnasamy
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.8982

Abstract

Efficient resource allocation is crucial in fog computing environments due to dynamic conditions and different user requirements; this work addresses the scheduling issues of internet of things (IoT) applications in such situations. Our proposed method, chaotic crossover tuna swarm optimizer (CCTSO), is based on metaheuristics and aims to reduce energy usage, reaction time, and SLA breaches; it should help with these problems. Improved system responsiveness and dependability are outcomes of the suggested approach's use of machine learning models for scheduling decision prediction and dynamic workload adaptation. The framework achieves a good balance between performance and energy efficiency by adjusting critical parameters and application settings. By reducing energy usage, reaction time, and operational cost while retaining reduced service level agreement (SLA) violation rates, our solution greatly outperforms previous techniques, according to experimental assessments. In real-world implementations, our results demonstrate that CCTSO is a strong solution for fog-based IoT scheduling, providing greater scalability and adaptability. Taken together, the results of this study provide a strong algorithmic foundation for better resource management in cloud, fog, and edge computing environments.
A review on radio-frequency transceiver architectures for low-power wireless sensor networks Narenahalli Ashok Kumar, Ambika; Mishra, Geetishree
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10633

Abstract

Wireless sensor networks (WSNs) have garnered significant scientific attention because of their many uses, but their power usage is a fundamental barrier to their deployment. Energy constraints have a direct effect on important design elements including battery capacity, energy harvester effectiveness, and network longevity. To enable sustainable WSN operation, radio-frequency (RF)-based transceiver (TR) design has become a key area of study. A thorough examination of current RF-TR architectures is given in this paper, with a focus on low-power (LP) implementations designed for WSN applications. Amplifier-sequenced hybrid (ASH), superheterodyne (SHD), zero-intermediate frequency (Zero-IF), low-intermediate frequency (Low-IF), sliding-intermediate frequency (Sliding-IF), and super-regenerative (SRG) architectures are among the TR system designs that are categorized, with an emphasis on the performance trade-offs associated with each. Comparative evaluation shows that Zero-IF and SRG architectures are more energy efficient than other designs that were studied, which makes them viable options for ultra-low-power (ULP) WSN installations. Along with outlining important research issues in RF-TR design, such as hardware minimization, security, synchronization, and energy optimization, this review also suggests possible future paths to improve the sustainability and performance of WSN-based RF-TRs.
Performance analysis of 3D assets in virtual reality simulations for climate change: a case study in sustainable energy systems Miranto, Cahya; Firmanda, Ardiman; Rante, Hestiasari; Sukaridhoto, Sritrusta; Agus Zainuddin, Muhammad; Rahman, Haolia
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9532

Abstract

This study investigates the performance impact of 3D assets in a virtual reality (VR) simulation designed for climate change education, aiming to balance visual fidelity and system efficiency on standalone headsets. Using a case study modeled on a sustainable energy environment, key performance metrics frames per second (FPS), triangle count, and draw calls were measured to assess the effect of object density, material transparency, and batching strategies. Experimental results show that configurations with 20 trees and 20 characters maintained 101 FPS, while denser scenes with 30 trees and 30 characters dropped to 79 FPS approaching the minimum usability threshold for VR. Transparent tree foliage with alpha-cutout materials imposed higher graphics processing unit (GPU) loads than high-triangle opaque character models, highlighting the performance cost of material complexity. These findings offer practical guidelines for optimizing asset configurations in immersive educational VR content. Future work may explore integration of artificial intelligence (AI) behavior and user interaction to assess broader system performance.
Hybrid DL and ML approach for MRI-based classification of bone marrow changes in lumbar vertebrae Shakir, Yasir Hussein; Kiong, Tiong Sieh; Chen, Chai Phing; Kumar, Sachin Sharma Ashok
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10617

Abstract

Alterations in the bone marrow changes lumbar vertebrae (BMCLVB) are considered important markers of chronic low back pain severity, particularly among patients with coexisting conditions like osteoporosis or cancer. Realizing these associations informs healthcare and insurance frameworks but also supports early intrusion planning for high-risk populations. This study aims to classification (BMCLVB) as normal or abnormal used magnetic resonance imaging (MRI) with machine learning (ML) model. A novel dataset comprising 1,018 BMCLVB MRI images was utilized to extract deep features via a pre-trained ConvNeXt-XLarge model. These features were then classified using different types in individual and ensemble ML algorithms. To ensure a comprehensive performance evaluation, all models were tested using accuracy, precision, recall, and F1-score. The combination of ConvNeXt-XLarge and logistic regression (LR) achieved a classification accuracy 93.14%, precision 93.22%, recall 94.83%, and F1-score 94.02%. These results highlight that the proposed model provides a fast and cost-efficient solution supporting the diagnosis of BMCLVB and potential to significantly improve clinical decision-making and patient care outcomes.
An innovative design of a frequency-tunable UHF RFID antenna for identification applications Errachidi, Zakaria; Zbitou, Jamal; Chahboun, Noha; Oulhaj, Otman; Lakhssassi, Ahmed
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9535

Abstract

This paper introduces the design of a new frequency-reconfigurable ultra-high frequency radio frequency identification (UHF RFID) antenna, demonstrating an innovative approach that enables dynamic adjustment of its resonance frequency. The proposed antenna design features a central dipole structure, enhanced by two hexagonal split-ring resonators (H-SRR) at each end. A T-match network is integrated into the center of the dipole, which is essential for achieving impedance matching between the antenna and the Alien Gen2 H4 RFID microchip. The antenna is designed using a Rogers 4350B substrate, a high-performance dielectric material ideal for RFID applications. With dimensions of 68×32.6×1.524 mm3, the compact antenna maintains full UHF band (860 MHz to 930 MHz) coverage compliant with International Telecommunications Union (ITU) RFID standards. This ensures that the antenna can be used in different regions around the world, offering broad compatibility with various RFID systems. The antenna's frequency reconfigurability is achieved through the integration of localized capacitors with variable values, which plays a key role in enabling precise adjustments to the antenna's center frequency across the entire UHF band. Extensive simulation results validate the effectiveness of this reconfigurable design, demonstrating that the antenna can dynamically adjust its frequency while maintaining excellent performance metrics, including impedance matching, radiation efficiency, and bandwidth. This makes the proposed antenna an ideal choice for modern RFID applications.
Improvement on the handover technique for 5G network using fuzzy logic algorithm Hakkou, Samia; Mazri, Tomader; Hmina, Nabil
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9795

Abstract

Beyond 5G (B5G) networks require advanced handover algorithms to guarantee seamless connectivity and optimum quality of service. Traditional handover methods are not sufficient to meet the stringent latency and reliability requirements of next-generation networks. To meet these challenges, the integration of fuzzy logic into handover algorithms offers a viable solution. The proposed approach utilizes parameters such as reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference plus noise ratio (SINR), and user equipment (UE) speed as inputs, while dynamically adjusting the time-to-trigger (TTT) and handover margin (HOM) as outputs. To assess the effectiveness of this algorithm, handover latency (HOL) and handover interruption time (HIT) are evaluated and compared with existing algorithms in the literature. The results show better and more efficient performance in both terms of latency and interruption time.

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